The best systems at the SemEval-16 and SemEval-17 community question answering shared tasks – a task that amounts to question relevancy ranking – involve complex pipelines and manual feature engineering. Despite this, many of these still fail at beating the IR baseline, i.e., the rankings provided by Google’s search engine. We present a strong baseline for question relevancy ranking by training a simple multi-task feed forward network on a bag of 14 distance measures for the input question pair. This baseline model, which is fast to train and uses only language-independent features, outperforms the best shared task systems on the task of retrieving relevant previously asked questions.

Discourse structure and coherence relations are one of the main inferential challenges addressed by computational pragmatics. The present study focuses on discourse markers as key elements in guiding the inferences of the statements in natural language. Through a rule-based approach for the automatic identification, classification and annotation of the discourse markers in a multilingual parallel corpus (Arabic-Spanish-English), this research provides a valuable resource for the community. Two main aspects define the novelty of the present study. First, it offers a multilingual computational processing of discourse markers, grounded on a theoritical framework and implemented in a XML tagging scheme. The XML scheme represents a set of pragmatic and grammatical attributes, considered as basic features for the different kinds of discourse markers. Besides, the scheme provides a typology of discourse markers based on their discursive functions including hypothesis, co-argumentation, cause, consequence, concession, generalization, topicalization, reformulation, enumeration, synthesis, etc. Second, Arabic language is addressed from a computational pragmatic perspective where the identification, classification and annotation processes are carried out using the information provided from the tagging of Spanish discourse markers and the alignments.